Kriging With Nonparametric Variance Function Estimation

The authors propose a method for fitting regression models to data that exhibit spatial correlation and heteroskedasticity. A combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model. The approach is demonstrated on a large dataset of predicted nitrogen runoff statistics from agricultural land in the Midwest and Northern Plains.